Given that CycleNet has large receptive fields, we hypothesise that it should classify images by the shape rather than texture of objects.
[1]} designed new benchmarks to explore the shape vs texture bias of deep neural networks trained on ImageNet (IN).
In the Stylised ImageNet dataset (SIN), the texture of each image is replaced with a randomly selected painting style.
In the Cue-Conflict dataset (CC), a few images are generated by iterative style transfer from selected textures consisting of patches of other classes.
We predict that our IN pre-trained CycleNet should transfer better than the baseline to both SIN and CC.
We generate SIN following the scripts at https://github.com/rgeirhos/Stylized-ImageNet, and data for CC is sourced from https://github.com/rgeirhos/texture-vs-shape.
Both experiments are evaluated at resolution 128x128.
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